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Lingbo Tong; Wen Qu; Zhiyong Zhang – Grantee Submission, 2025
Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method.…
Descriptors: Factor Analysis, Monte Carlo Methods, Statistical Analysis, Sample Size
Liu, Yan; Zumbo, Bruno D. – Educational and Psychological Measurement, 2012
There is a lack of research on the effects of outliers on the decisions about the number of factors to retain in an exploratory factor analysis, especially for outliers arising from unintended and unknowingly included subpopulations. The purpose of the present research was to investigate how outliers from an unintended and unknowingly included…
Descriptors: Factor Analysis, Factor Structure, Evaluation Research, Evaluation Methods
Martin, Andrew J.; Yu, Kai; Papworth, Brad; Ginns, Paul; Collie, Rebecca J. – Journal of Psychoeducational Assessment, 2015
This study explored motivation and engagement among North American (the United States and Canada; n = 1,540), U.K. (n = 1,558), Australian (n = 2,283), and Chinese (n = 3,753) secondary school students. Motivation and engagement were assessed via students' responses to the Motivation and Engagement Scale-High School (MES-HS). Confirmatory factor…
Descriptors: Foreign Countries, Motivation, Learner Engagement, Secondary School Students
Jones-Farmer, L. Allison – Structural Equation Modeling: A Multidisciplinary Journal, 2010
When comparing latent variables among groups, it is important to first establish the equivalence or invariance of the measurement model across groups. Confirmatory factor analysis (CFA) is a commonly used methodological approach to examine measurement equivalence/invariance (ME/I). Within the CFA framework, the chi-square goodness-of-fit test and…
Descriptors: Factor Structure, Factor Analysis, Evaluation Research, Goodness of Fit
de Winter, J. C. F.; Dodou, D.; Wieringa, P. A. – Multivariate Behavioral Research, 2009
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…
Descriptors: Sample Size, Factor Analysis, Enrollment, Evaluation Methods
Stellefson, Michael; Hanik, Bruce – Online Submission, 2008
When conducting an exploratory factor analysis, the decision regarding the number of factors to retain following factor extraction is one that the researcher should consider very carefully, as the decision can have a dramatic effect on results. Although there are numerous strategies that can and should be utilized when making this decision,…
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Evaluation Methods
Shujuan, Wang; Meihua, Qian; Jianxin, Zhang – Journal of Psychoeducational Assessment, 2009
This article examines the psychometric structure of the Anxiety Control Questionnaire (ACQ) in Chinese adolescents. With the data collected from 212 senior high school students (94 females, 110 males, 8 unknown), seven models are tested using confirmatory factor analyses in the framework of the multitrait-multimethod strategy. Results indicate…
Descriptors: Multitrait Multimethod Techniques, Factor Structure, Adolescents, Measures (Individuals)

Bentler, Peter M. – Structural Equation Modeling, 2000
Discusses issues related to model evaluation in structural equation modeling. Supports nested model comparisons via sequential chi-square difference tests as consistent with the four-step approach to model evaluation when models of the factor analytic simultaneous equation type are entertained. (Author/SLD)
Descriptors: Chi Square, Evaluation Methods, Factor Analysis, Factor Structure
Hogarty, Kristine Y.; Hines, Constance V.; Kromrey, Jeffrey D.; Ferron, John M.; Mumford, Karen R. – Educational and Psychological Measurement, 2005
The purpose of this study was to investigate the relationship between sample size and the quality of factor solutions obtained from exploratory factor analysis. This research expanded upon the range of conditions previously examined, employing a broad selection of criteria for the evaluation of the quality of sample factor solutions. Results…
Descriptors: Sample Size, Factor Analysis, Factor Structure, Evaluation Methods
Darom, Efraim – 1982
In an analysis of multitrait-multimethod matrices the criteria for discriminant validity are shown to include a "structure" criterion as an invariance of traits structure to methods. The criterion is meant to fit data to an additive model with traits and methods but not interaction terms. The importance of the structure criterion and the…
Descriptors: Discriminant Analysis, Evaluation Methods, Factor Structure, Mathematical Models

Heesacker, Martin; Heppner, P. Paul – Journal of Counseling Psychology, 1983
Examined the factor structure of the Counselor Rating Form (CRF). Real clients (N=110) completed the CRF at the conclusion of counseling. The results suggest the existence of only one major factor underlying clients' perceptions of counselors. The one-factor model performed as well as the three-factor model. (Author/JAC)
Descriptors: Counselor Characteristics, Counselor Evaluation, Counselor Performance, Evaluation Methods

Floyd, Frank J.; Widaman, Keith F. – Psychological Assessment, 1995
The goals of exploratory and confirmatory factor analysis are described, and procedural guidelines for each approach are summarized, emphasizing the use of factor analysis in developing and refining clinical measures. Recent examples of the use of factor analysis are reviewed and used to highlight controversies that can emerge. (SLD)
Descriptors: Clinical Diagnosis, Evaluation Methods, Factor Structure, Measures (Individuals)